Abstract
In this article, we describe four sequential sampling strategies for estimating the quantile of Y = f(X), where X has a known distribution in and f is a deterministic unknown, expensive-to-evaluate real-valued function. These approaches all consist in modeling f as a sample of a well-chosen Gaussian process and aim at estimating the quantile by using as few evaluations of f as possible. The different methodologies are first compared through various numerical experiments. Then, in the framework of the ANR-JST FETUS project, we apply our strategies to a real example corresponding to the exposure of a Japanese pregnant-woman model and her 26-week-old fetus to a plane wave. Finally, we compare our methodologies on a simplified geometric model designed for modeling the fetus exposure to plane waves. Supplementary materials for this article are available online.
| Original language | English |
|---|---|
| Pages (from-to) | 30-42 |
| Number of pages | 13 |
| Journal | Technometrics |
| Volume | 58 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2 Jan 2016 |
Keywords
- Design of experiments
- Gaussian process
- Quantile estimation
- Sequential approach
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